Untargeted metabolomics predicts the functional outcome of ischemic stroke
Autor: | Yih Ru Chen, Ting An Shen, Lung Chan, Yu Wei Wu, Chaur Jong Hu, Chen Yang Lee, Nai Fang Chi, Chung Y. Hsu, Hung Yi Chiou, Tzu Hao Chang |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Oncology
medicine.medical_specialty Multivariate analysis Metabolite Brain Ischemia Proinflammatory cytokine 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Metabolomics Modified Rankin Scale Internal medicine medicine Humans Eosinophil degranulation Stroke Outcome lcsh:R5-920 Ischemic stroke Platelet-activating factor business.industry Recovery of Function General Medicine medicine.disease chemistry 030220 oncology & carcinogenesis 030211 gastroenterology & hepatology business lcsh:Medicine (General) |
Zdroj: | Journal of the Formosan Medical Association, Vol 120, Iss 1, Pp 234-241 (2021) |
ISSN: | 0929-6646 |
Popis: | Background/purpose Metabolites in blood have been found associated with the occurrence of vascular diseases, but its role in the functional recovery of stroke is unclear. The aim of this study is to investigate whether the untargeted metabolomics at the acute stage of ischemic stroke is able to predict functional recovery. Methods One hundred and fifty patients with acute ischemic stroke were recruited and followed up for 3 months. Fasting blood samples within 7 days of stroke were obtained, liquid chromatography and mass spectrometry were applied to identify outcome-associated metabolites. The patients’ clinical characteristics and identified metabolites were included for constructing the outcome prediction model using machine learning approaches. Results By using multivariate analysis, 220 differentially expressed metabolites (DEMs) were discovered between patients with favorable outcomes (modified Rankin Scale, mRS ≤ 2 at 3 months, n = 77) and unfavorable outcomes (mRS ≥ 3 at 3 months, n = 73). After feature selection, 63 DEMs were chosen for constructing the outcome prediction model. The predictive accuracy was below 0.65 when including patients' clinical characteristics, and could reach 0.80 when including patients' clinical characteristics and 63 selected DEMs. The functional enrichment analysis identified platelet activating factor (PAF) as the strongest outcome-associated metabolite, which involved in proinflammatory mediators release, arachidonic acid metabolism, eosinophil degranulation, and production of reactive oxygen species. Conclusion Metabolomics is a potential method to explore the blood biomarkers of acute ischemic stroke. The patients with unfavorable outcomes had a lower PAF level compared to those with favorable outcomes. |
Databáze: | OpenAIRE |
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